1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
1/12/2023 Diosi 168000 Tami Consulta vet + hospitalización + exámenes
2/12/2023 Comida 83183 Tami Supermercado
2/12/2023 Farmacia 36819 Tami Diolasa + Clotrimazol + Mulcatel + Propolgea
7/12/2023 Diosi 30000 Tami Consulta Veterinaria
8/12/2023 Agua 15080 Andrés NA
9/12/2023 Comida 57905 Tami Supermercado
9/12/2023 Comida 20614 Tami Fork pedido el 28/11
17/12/2023 Comida 40000 Andrés NA
17/12/2023 Comida 8000 Andrés almuerzo
17/12/2023 VTR 22000 Andrés NA
17/12/2023 Comida 42000 Andrés piwen
17/12/2023 Comida 48432 Tami Supermercado
17/12/2023 Enceres 16400 Tami Incoludido
19/12/2023 Aporte Basureros 10000 Tami NA
22/12/2023 Netflix 8326 Tami NA
22/12/2023 Diosi 14700 Andrés Antiparasitario
24/12/2023 Comida 79633 Tami Supermercado
24/12/2023 Comida 19950 Andrés Empanadas
25/12/2023 Uber 5595 Tami NA
27/12/2023 Diosi 22970 Andrés arena
30/12/2023 Comida 50000 Andrés jumbo la litad de los 97
2/1/2024 Electricidad 55466 Andrés pac enel
2/1/2024 Comida 44542 Tami Supermercado
5/1/2024 Comida 9516 Tami Burger King pre Maite
5/1/2024 Comida 19590 Tami Barritas Soul
8/1/2024 Comida 57625 Tami Supermercado
13/1/2024 Limpieza alfombras 60000 Tami NA
13/1/2024 Comida 33110 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.5787e+08   2    8.0551  3e-04 ***
## lag_depvar    9.6956e+10   1 1820.7627 <2e-16 ***
## Residuals     3.4932e+10 656                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr    p adj
## 1-0  7228.838   904.3011 13553.38 0.020309
## 2-0 29635.497 23893.8216 35377.17 0.000000
## 2-1 22406.659 19026.0521 25787.27 0.000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
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## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
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## 269  51907.43             2   47160.57
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## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
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## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
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## 285  69120.71             2   62790.29
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## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
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## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
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## 311  36435.14             2   37898.00
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## 313  34541.86             2   30209.57
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## 318  62730.57             2   65201.86
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## 320  73744.86             2   64589.14
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## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
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## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
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## 337  34076.14             2   34975.43
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## 339  28862.57             2   34221.14
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## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
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## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
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## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
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## 381  44864.57             2   33035.71
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## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
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## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
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## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
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## 450  45623.14             2   39153.71
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## 452  41027.71             2   40627.43
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## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   504 51869.76 15474.480
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1926.16446   4000.63980   -498.59715   2472.29262  -2891.57278    547.11832 
##            8            9           10           11           12           13 
##  -5614.13268  -1234.79685  -4018.01934   -519.25218  -5026.53680  -1757.02422 
##           14           15           16           17           18           19 
##  -1046.39929    243.21485  -3345.55472   -511.79427  -2244.73052   6477.87924 
##           20           21           22           23           24           25 
##  -1523.93330  -1220.08272   1454.14205  -1172.95837    235.75917   1708.25751 
##           26           27           28           29           30           31 
##  -7054.45133    882.39726   8159.44816    531.09810    100.06640  -2292.52927 
##           32           33           34           35           36           37 
##   1640.24356   4662.67653   1288.60431   2559.67804  -1673.50494   4756.19035 
##           38           39           40           41           42           43 
##   4391.20254  -2128.35730  -2888.88150  -1076.94434 -10729.73937   7125.86890 
##           44           45           46           47           48           49 
##   2533.44913   1388.27273   8146.79630    854.87320   6688.06947   6960.36318 
##           50           51           52           53           54           55 
##  -5557.61913  -4606.46133  -4971.71978  -7933.08513   5997.84757  -4091.69253 
##           56           57           58           59           60           61 
##  -4972.99612   3708.28449    822.20756    -74.36254    105.47416  -5025.54820 
##           62           63           64           65           66           67 
##  18020.90578   3843.17108  -3409.29177   6073.87318   7570.82537  14957.14298 
##           68           69           70           71           72           73 
##   2209.88950 -12732.83473  -1100.39440   4802.95655  -4684.74361  -4294.87494 
##           74           75           76           77           78           79 
## -10472.23157   2319.57388  -5487.49253    900.46194  -6990.82790    327.65218 
##           80           81           82           83           84           85 
##  -2536.43557  -2890.07151  -4146.10835   -787.55678   2088.27127   3603.18539 
##           86           87           88           89           90           91 
##    399.20536   -544.03278    137.46370   4254.12460  -1134.40882   1157.70357 
##           92           93           94           95           96           97 
##  -2039.13162  -1054.88371    152.12768    256.12935  -7495.17584   2261.62080 
##           98           99          100          101          102          103 
##  -8676.69003  -3143.75590  -4263.61676  -1996.94765  -1515.80635   2939.16814 
##          104          105          106          107          108          109 
##  -2501.00535   2418.04959  -1269.41154    855.49986   2503.10889  -3185.16673 
##          110          111          112          113          114          115 
##  -4800.21969   -993.30501   1765.58437  11604.59875  -1130.37491   2747.23097 
##          116          117          118          119          120          121 
##   4375.56760   3670.95984   -895.40129  -4554.57929  -3658.24192   2317.89215 
##          122          123          124          125          126          127 
##  -1695.92653   1345.30493   8885.02261   1014.66035    291.38755  -2377.70870 
##          128          129          130          131          132          133 
##   2740.52603   7170.97524   1230.88487  -8291.33800   1794.25468   4203.96742 
##          134          135          136          137          138          139 
##  -3036.43947  -1358.73124   -822.86492  -3865.85162   1133.42919   -518.89953 
##          140          141          142          143          144          145 
##  -2941.30347   1647.60132  -1914.16483  -7887.87807   1862.37438  -3600.70006 
##          146          147          148          149          150          151 
##   1940.94387   -363.69304    926.74475   -426.19092   1288.62734   1153.65660 
##          152          153          154          155          156          157 
##   3347.65570  -4814.58290  -1211.15349  -3286.13599   5861.12656   9760.19194 
##          158          159          160          161          162          163 
##  -3630.28689  -5007.43002   3326.38615     10.17688   2537.60649  -6006.23086 
##          164          165          166          167          168          169 
##  -6926.92157   3890.65868  17226.02267   3753.71232   -233.46316  -2310.22383 
##          170          171          172          173          174          175 
##  -1026.29484   3638.95265   -131.43126  -7996.77224   2802.54741   4314.87252 
##          176          177          178          179          180          181 
##    681.99839   8807.03972  -9063.94114  -3458.90271 -10790.72853 -11452.32793 
##          182          183          184          185          186          187 
##    869.87499   8991.37462  -1550.36422   5797.97569   6530.31494  13233.78885 
##          188          189          190          191          192          193 
##   8695.08455  -3709.18746   2702.44264  10606.11180  -1284.40831  -2164.92906 
##          194          195          196          197          198          199 
## -10082.34493  -6356.41049   1134.89231  -5306.25815  -9945.95845   5096.83716 
##          200          201          202          203          204          205 
##  -3239.75657  -1913.94521  -1010.95024   6296.72553   9802.45938    651.53103 
##          206          207          208          209          210          211 
##   2989.59183   3191.70471   5905.99839  13020.60777  -5336.08697 -11083.73389 
##          212          213          214          215          216          217 
##  -5659.85216 -10673.80630  -5317.10075   1233.91892 -13247.03795  15980.57167 
##          218          219          220          221          222          223 
##   7697.23917   1542.54054  26717.24873  12947.18067   7882.16175  14605.50930 
##          224          225          226          227          228          229 
##  -3205.95034  -1192.21980   4221.20247    796.77927   3125.78447   9371.55163 
##          230          231          232          233          234          235 
##   6282.74301  -1426.47649  -1443.39655   9728.11305 -11106.87302  -7114.47041 
##          236          237          238          239          240          241 
##  -8510.66330 -10210.70694   2823.12973   1172.04940  -8438.52199  -9243.94141 
##          242          243          244          245          246          247 
##   8731.65617  -7948.77290   2204.57398 -10514.97773  -4401.54053   1053.25265 
##          248          249          250          251          252          253 
##    697.28370 -12572.75875   3227.75284   1749.69202   3964.25496   1976.05180 
##          254          255          256          257          258          259 
##  -1273.48361  11016.38722  20932.96840   3543.55207  -3932.30543   4331.16846 
##          260          261          262          263          264          265 
##  -1449.74833   3915.88060  -4649.56759 -10798.80888  -4816.85057   -677.03365 
##          266          267          268          269          270          271 
##  -5339.33246   8561.13469  -4348.52238   4057.20025  -2169.81443   4335.22113 
##          272          273          274          275          276          277 
##    680.98479   7278.65877  -1337.03753  12058.38568  -4392.38174   1812.32591 
##          278          279          280          281          282          283 
##   -283.72835   7911.05693  -4903.81494  -2682.20453 -11268.51933  -2846.87696 
##          284          285          286          287          288          289 
##  18453.24945   7866.08084   2905.67290   -452.45019   1038.09214   6514.06331 
##          290          291          292          293          294          295 
##   7062.29205 -18530.26607 -11204.20522  -8342.68142   9352.08086   2930.14058 
##          296          297          298          299          300          301 
##  -1266.78472  27301.23306  10352.52110   5279.94603   9904.47690   3314.08370 
##          302          303          304          305          306          307 
##   -606.37481   8241.21484 -23896.32397  -3533.37978   -229.32404  -7023.51532 
##          308          309          310          311          312          313 
##  -4121.46832   2743.00478  -9320.28256  -3471.28959  -8442.28084   1229.19919 
##          314          315          316          317          318          319 
##  -3423.41228   1766.30315  -4302.51779  27194.69247   -592.28155   3385.72176 
##          320          321          322          323          324          325 
##  10947.48213   5828.18655  32654.13923   5780.37352 -20294.89774   2057.67730 
##          326          327          328          329          330          331 
##   1357.46072  -6242.98302  -1635.86394 -33211.75104    512.75189  -2604.37769 
##          332          333          334          335          336          337 
##   -380.17038  -3412.34391   3836.50831   -593.98911  -7090.25398  -3323.82461 
##          338          339          340          341          342          343 
##  -2407.57635  -7890.50314   3572.41760  -1557.69718  -1913.14358  -1164.30609 
##          344          345          346          347          348          349 
##     20.29400    352.65897  -1720.38365  -9553.14506 -13423.91883   1954.49181 
##          350          351          352          353          354          355 
##  -4596.54893  -3947.61654  -6273.71767   1422.51127   1126.67333   2550.56827 
##          356          357          358          359          360          361 
##  -3904.78396   -684.09432    526.60048   6892.85324    262.74623    -46.94426 
##          362          363          364          365          366          367 
##   2574.78159  -2724.32786   -889.49517  -8763.59792  -4751.90089  -6376.21671 
##          368          369          370          371          372          373 
##  -5165.74730  -7497.79124   4715.39675    178.36916   6960.11170  -7682.65267 
##          374          375          376          377          378          379 
##  -2397.05231  -3528.72793  -2627.55633 -12623.13617   1606.41578 -10862.01041 
##          380          381          382          383          384          385 
##   5368.78966   9128.14788   3065.01733  -2412.00433   1563.14913   6728.33973 
##          386          387          388          389          390          391 
##  11480.14411  -5603.84692  -5271.46113   -151.07257   8563.13782   1917.20265 
##          392          393          394          395          396          397 
##  11326.93528  -9655.17808   2834.32232    792.41995    633.43734   -591.97274 
##          398          399          400          401          402          403 
##   -524.16118 -14466.58227   8362.08621  -1211.41009  -1413.47272   6929.46304 
##          404          405          406          407          408          409 
##  -7892.34241  -1359.26231  -2598.53661  -5904.26960  -2998.83151  -4067.27021 
##          410          411          412          413          414          415 
##  -8927.26685   5883.57232   1504.18223  -7467.85539  -7860.06881  13986.93804 
##          416          417          418          419          420          421 
##   3787.57717   4510.95290  -7967.90150  -4783.20963  -2689.18799   2716.43799 
##          422          423          424          425          426          427 
## -14062.28914  -3002.28870  -9308.85408   2726.95580   6772.18833   6486.22161 
##          428          429          430          431          432          433 
##  -3983.28235  -4163.34055  -4805.40223  -1914.56867  -5836.93576  -6799.59090 
##          434          435          436          437          438          439 
##  -6174.71247  -1654.82424  -1085.07862  -5185.07511   2344.69554   4671.12189 
##          440          441          442          443          444          445 
##  -5139.29026  -2287.34126   1443.02448  -3928.66944   2713.92150  -6644.25176 
##          446          447          448          449          450          451 
## -12243.82191  -4770.19706   9372.46258  -2142.31273   4639.78074  -5904.26539 
##          452          453          454          455          456          457 
##  -1219.53935    292.30921   2957.86714 -12284.52429   3208.34956  -6792.11279 
##          458          459          460          461          462          463 
##   6366.34256   2964.75207   2508.77573  -3808.09379   2082.56165     15.09087 
##          464          465          466          467          468          469 
##   1817.22496   -473.16424   3390.09160  -2558.66992   5847.00324  -6828.26400 
##          470          471          472          473          474          475 
##  -2945.84362  -2218.81567  -4695.11989   2919.10360   7775.32396  -5932.20841 
##          476          477          478          479          480          481 
##   1489.28457  -6148.67239  -2888.05187   1946.59456 -12953.37133  -9930.91796 
##          482          483          484          485          486          487 
##  -1469.62261   -217.79333  -1159.68429  -1516.83010  -9745.11629  10842.95182 
##          488          489          490          491          492          493 
##   6165.28641   7445.56300  -5314.72332   5410.42954   9404.32305   6272.83987 
##          494          495          496          497          498          499 
## -13202.92934 -10495.75998  -3506.06891  -1196.98292   -608.58920  -7696.92219 
##          500          501          502          503          504          505 
##    462.31343   4176.17802   5475.42619    709.62863    139.44249  -7179.10810 
##          506          507          508          509          510          511 
##    538.35496  -5059.66756   1767.47099  -1320.55433  -8186.47255   -721.75180 
##          512          513          514          515          516          517 
##  -2776.53700   -700.98141   1235.79841  -9552.75528  -7927.32856  24056.10386 
##          518          519          520          521          522          523 
##   9943.49851   6113.19496  -5052.83405   2981.01060  17218.62101  11867.11001 
##          524          525          526          527          528          529 
## -23659.31802  -4938.21767  -3685.92754   4572.56357   -291.84424 -11044.87891 
##          530          531          532          533          534          535 
##   4307.41667  13900.03923  -4802.41202   4466.53949   5695.89576  -1593.41939 
##          536          537          538          539          540          541 
##  -4388.39058  -6993.90311  -2113.92055   8292.00219    213.67874  -8058.42893 
##          542          543          544          545          546          547 
##   1791.64157   -589.92805    374.53461 -11011.52037 -11177.07256   1808.94721 
##          548          549          550          551          552          553 
##   6837.90678  -1363.31126    788.88201  -7743.36210   8455.58468    929.32275 
##          554          555          556          557          558          559 
## -11905.51658   9051.45030   8685.38786    238.79918   4980.86265  -3396.89107 
##          560          561          562          563          564          565 
##  14221.92612  21780.33714  -5947.87993  -9311.51309   6975.61198    489.75350 
##          566          567          568          569          570          571 
##   3689.62154  -7125.82685 -17178.25284   6564.28545   6443.32972   1999.79708 
##          572          573          574          575          576          577 
##   3214.57531   1917.57694  -2007.24822  14833.53589  -9354.97341  -6107.70183 
##          578          579          580          581          582          583 
##   8756.93324   3015.61070  -6366.68565   7587.15238  -3629.47692  -2669.51386 
##          584          585          586          587          588          589 
##  15766.64179 -14237.02376   8476.29440    226.88885  -6068.61210   -700.67050 
##          590          591          592          593          594          595 
##    297.50748 -10601.64430   1715.01156  -7182.28137   2953.75488   8817.19586 
##          596          597          598          599          600          601 
##  -7420.50013   5833.86053   2803.65941   6958.92342  -3004.80592   6277.59221 
##          602          603          604          605          606          607 
##  -8100.25867   2329.54051   1372.52722   3252.63097   1643.68837    554.39315 
##          608          609          610          611          612          613 
##  -5660.17067   8140.11246  -1013.50502  -2431.96900  -3352.80149  -8174.66383 
##          614          615          616          617          618          619 
##  11910.53197   5060.26438  -9132.99554  11674.02936   6252.41196  -5308.78705 
##          620          621          622          623          624          625 
##  26527.71089 -12325.22497  -6500.28979   3323.35642  -3962.33671 -10463.96546 
##          626          627          628          629          630          631 
##  11281.69547 -21488.57963  -2559.48058   8531.02485  11137.23322  -1392.67340 
##          632          633          634          635          636          637 
##  33414.48338  -6024.82311   6129.24634   5842.93426  -1801.49660  -4958.59695 
##          638          639          640          641          642          643 
##  -1663.53251 -12204.94724  -2198.39900  -1865.33857  -2514.56586  -2873.64971 
##          644          645          646          647          648          649 
##   1776.50376   4434.21562  17038.77886  18853.07799   1399.00419   5257.72872 
##          650          651          652          653          654          655 
##  11091.74201  20717.98307   1518.67240 -27368.44572  -1098.07886  -2082.82064 
##          656          657          658          659          660          661 
##   2034.36414  -3006.44255 -10487.22038   1654.49998   4256.39308   -907.30751 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17343.12 20138.36 24314.74 24037.85 26348.29 23729.60 24432.85 19751.94 
##       10       11       12       13       14       15       16       17 
## 19493.31 16884.54 17647.82 14436.88 14487.11 15139.64 16805.27 15155.94 
##       18       19       20       21       22       23       24       25 
## 16171.73 15556.69 22509.93 21610.65 21100.00 22955.53 22293.81 22934.46 
##       26       27       28       29       30       31       32       33 
## 24746.74 18785.89 20480.55 28174.90 28231.51 27910.39 25583.04 26959.89 
##       34       35       36       37       38       39       40       41 
## 30732.82 31074.89 32458.36 30014.38 34051.80 37201.36 34311.17 31180.23 
##       42       43       44       45       46       47       48       49 
## 30049.03 20800.42 28181.98 30574.01 31643.35 38356.70 37860.50 42437.64 
##       50       51       52       53       54       55       56       57 
## 46596.62 39427.75 34095.29 29208.80 22478.30 28653.55 25296.57 21661.72 
##       58       59       60       61       62       63       64       65 
## 25989.65 27226.22 27517.81 27922.12 23868.38 40156.97 41967.29 37299.98 
##       66       67       68       69       70       71       72       73 
## 41430.17 46256.14 56729.68 54779.69 40292.11 37843.47 40806.32 35210.45 
##       74       75       76       77       78       79       80       81 
## 30745.66 21618.71 24761.78 20761.82 22809.83 17798.49 19777.15 19017.79 
##       82       83       84       85       86       87       88       89 
## 18063.25 16167.41 17421.87 20964.10 25301.22 26273.03 26297.54 26903.02 
##       90       91       92       93       94       95       96       97 
## 30952.84 29804.73 30785.85 28885.60 28100.02 28461.44 28860.60 22555.24 
##       98       99      100      101      102      103      104      105 
## 25515.26 18672.90 17549.90 15626.38 15920.66 16585.69 20976.72 20076.95 
##      106      107      108      109      110      111      112      113 
## 23523.98 23317.79 24963.32 27787.60 25331.36 21839.73 22110.13 24708.12 
##      114      115      116      117      118      119      120      121 
## 35374.37 33600.20 35404.15 38347.75 40267.97 37998.58 32914.10 29322.25 
##      122      123      124      125      126      127      128      129 
## 31367.07 29678.41 30838.41 38299.48 37948.47 37027.14 33947.90 35696.60 
##      130      131      132      133      134      135      136      137 
## 40995.97 40446.48 31808.75 33050.46 36182.01 32658.16 31074.86 30176.57 
##      138      139      140      141      142      143      144      145 
## 26796.43 28185.04 27958.87 25687.40 27674.88 26324.74 20043.63 23018.84 
##      146      147      148      149      150      151      152      153 
## 20885.20 23807.98 24338.11 25899.48 26078.23 27702.20 28979.20 31956.01 
##      154      155      156      157      158      159      160      161 
## 27508.87 26785.28 24385.16 30171.67 41650.72 40011.43 37424.47 42353.11 
##      162      163      164      165      166      167      168      169 
## 43735.96 47089.52 42638.21 38031.06 43357.26 59361.86 61533.61 59976.65 
##      170      171      172      173      174      175      176      177 
## 56860.29 55288.76 57942.00 56983.92 49416.74 52188.70 55863.00 55898.53 
##      178      179      180      181      182      183      184      185 
## 62897.23 53572.90 50383.16 41359.61 33053.41 36497.63 46416.65 45882.60 
##      186      187      188      189      190      191      192      193 
## 51726.69 57366.78 67952.92 73139.33 66949.13 67139.03 74080.27 69835.64 
##      194      195      196      197      198      199      200      201 
## 65440.20 54880.41 49019.54 50417.83 46092.96 38404.73 44712.19 42971.95 
##      202      203      204      205      206      207      208      209 
## 42616.52 43086.13 49756.11 58483.04 58119.41 59812.72 61438.29 65160.25 
##      210      211      212      213      214      215      216      217 
## 74453.94 66681.31 55086.00 49793.23 40953.96 37967.22 41024.04 31226.43 
##      218      219      220      221      222      223      224      225 
## 47890.05 55077.17 55962.61 78312.39 85670.55 87637.20 95089.95 86206.08 
##      226      227      228      229      230      231      232      233 
## 80314.08 79903.65 76614.79 75791.59 80442.11 81781.48 76318.54 71618.89 
##      234      235      236      237      238      239      240      241 
## 77169.30 64060.90 56242.81 48340.42 40105.16 44220.52 46333.95 39904.23 
##      242      243      244      245      246      247      248      249 
## 33699.20 43793.92 38145.85 42009.69 34414.83 33144.32 36732.86 39505.19 
##      250      251      252      253      254      255      256      257 
## 30502.10 36331.74 40063.75 45163.66 47832.34 47334.18 57447.03 74624.73 
##      258      259      260      261      262      263      264      265 
## 74443.16 67875.97 69330.75 65620.55 67040.28 60911.95 50382.42 46482.32 
##      266      267      268      269      270      271      272      273 
## 46687.90 42865.72 51509.09 47850.23 51921.24 50072.21 54065.30 54355.91 
##      274      275      276      277      278      279      280      281 
## 60263.47 57940.90 67437.24 61472.96 61679.16 60058.37 65696.39 59541.35 
##      282      283      284      285      286      287      288      289 
## 56167.95 45911.02 44337.04 61254.63 66683.76 67085.74 64550.48 63654.51 
##      290      291      292      293      294      295      296      297 
## 67582.42 71421.27 52764.78 43047.54 37167.92 47300.86 50483.50 49613.62 
##      298      299      300      301      302      303      304      305 
## 73368.19 79205.05 79860.52 84388.77 82620.23 77741.21 81144.75 56501.81 
##      306      307      308      309      310      311      312      313 
## 52831.18 52516.80 46420.33 43680.71 47218.28 39906.43 38651.85 33312.66 
##      314      315      316      317      318      319      320      321 
## 37028.13 36224.41 39985.95 38007.16 63322.85 61203.42 62797.38 70649.53 
##      322      323      324      325      326      327      328      329 
## 72993.29 98009.91 96417.18 72688.47 71508.25 69895.55 61994.15 59168.89 
##      330      331      332      333      334      335      336      337 
## 29665.68 33285.95 33717.46 35995.06 35347.92 41009.70 42065.68 37399.97 
##      338      339      340      341      342      343      344      345 
## 36628.72 36753.07 32157.44 38046.98 38698.29 38952.02 39811.85 41565.20 
##      346      347      348      349      350      351      352      353 
## 43353.96 43110.15 36183.49 26923.37 32170.55 31052.33 30649.86 28309.77 
##      354      355      356      357      358      359      360      361 
## 32903.33 36589.15 40971.36 39193.38 40430.69 42530.15 49790.54 50331.09 
##      362      363      364      365      366      367      368      369 
## 50529.08 52947.33 50476.64 49931.31 42710.62 39958.50 36205.18 34024.36 
##      370      371      372      373      374      375      376      377 
## 30154.03 37309.06 39554.32 47296.08 41377.62 40834.87 39398.84 38940.14 
##      378      379      380      381      382      383      384      385 
## 29974.30 34488.58 27666.92 35736.42 45881.13 49381.58 47686.42 49641.80 
##      386      387      388      389      390      391      392      393 
## 55748.57 65061.13 58396.18 52965.22 52698.86 59943.94 60457.78 68968.46 
##      394      395      396      397      398      399      400      401 
## 58272.68 59811.01 59379.13 58872.40 57386.88 56171.01 43170.91 51600.12 
##      402      403      404      405      406      407      408      409 
## 50618.76 49603.82 55888.49 48566.83 47890.54 46247.70 42003.69 40855.70 
##      410      411      412      413      414      415      416      417 
## 38954.84 33156.57 40885.96 43759.00 38528.35 33706.06 48306.85 52081.62 
##      418      419      420      421      422      423      424      425 
## 55939.33 48545.64 44935.90 43635.99 47157.15 35787.15 35521.28 29884.62 
##      426      427      428      429      430      431      432      433 
## 35372.67 43548.64 50315.28 47139.63 44261.69 41242.85 41133.08 37675.02 
##      434      435      436      437      438      439      440      441 
## 33883.71 31168.11 32715.51 34531.22 32572.16 37349.74 43442.29 40253.77 
##      442      443      444      445      446      447      448      449 
## 39965.12 42916.81 40841.36 44758.25 40091.68 31287.20 30145.82 41296.03 
##      450      451      452      453      454      455      456      457 
## 40983.36 46531.69 42247.25 42590.55 44181.56 47832.10 37890.65 42651.68 
##      458      459      460      461      462      463      464      465 
## 38158.23 45589.53 49045.51 51618.38 48407.44 50705.62 50903.49 52618.74 
##      466      467      468      469      470      471      472      473 
## 52125.48 55015.67 52392.57 57351.84 50734.42 48388.82 47000.69 43686.47 
##      474      475      476      477      478      479      480      481 
## 47374.25 54701.78 49230.14 50902.39 45786.05 44194.55 46975.94 36582.78 
##      482      483      484      485      486      487      488      489 
## 30261.48 32096.79 34744.40 36207.26 37155.54 30912.05 43214.29 49753.29 
##      490      491      492      493      494      495      496      497 
## 56459.29 51267.00 56012.11 63506.87 67248.93 53755.33 44504.64 42565.55 
##      498      499      500      501      502      503      504      505 
## 42882.87 43659.64 38246.69 40601.96 45807.00 51385.23 52081.99 52190.54 
##      506      507      508      509      510      511      512      513 
## 46007.07 47322.67 43649.96 46355.27 46027.04 39857.18 40967.68 40157.84 
##      514      515      516      517      518      519      520      521 
## 41243.34 43835.33 36805.76 32171.04 55625.93 63638.09 67224.55 60724.13 
##      522      523      524      525      526      527      528      529 
## 62039.24 75377.60 82227.32 57633.50 52596.93 49351.44 53650.70 53166.02 
##      530      531      532      533      534      535      536      537 
## 43528.30 48429.25 60859.27 55479.89 58815.68 62730.85 59837.10 54958.33 
##      538      539      540      541      542      543      544      545 
## 48539.63 47220.00 55012.61 54767.57 47463.07 49646.21 49476.04 50157.23 
##      546      547      548      549      550      551      552      553 
## 40976.50 32960.91 37223.66 45192.45 44993.12 46667.93 40786.84 49635.68 
##      554      555      556      557      558      559      560      561 
## 50769.95 40735.26 50102.47 57822.06 57198.57 60730.75 56575.07 68121.38 
##      562      563      564      565      566      567      568      569 
## 84506.02 74777.51 63549.39 67888.10 66046.66 67211.68 58935.25 43216.00 
##      570      571      572      573      574      575      576      577 
## 50096.96 55894.49 57055.71 59093.42 59728.68 56907.46 68930.97 58497.99 
##      578      579      580      581      582      583      584      585 
## 52335.35 59798.39 61274.97 54494.85 60647.19 56303.94 53402.36 66725.17 
##      586      587      588      589      590      591      592      593 
## 52419.28 59629.68 58738.61 52575.24 51893.06 52164.07 43049.13 45795.00 
##      594      595      596      597      598      599      600      601 
## 40519.39 44687.80 53291.36 46744.14 52496.34 54830.79 60396.52 56624.69 
##      602      603      604      605      606      607      608      609 
## 61350.69 53073.03 54918.76 55680.94 57947.03 58510.61 58059.74 52343.32 
##      610      611      612      613      614      615      616      617 
## 59276.22 57371.68 54521.80 51287.95 44379.18 55679.59 59496.14 50596.83 
##      618      619      620      621      622      623      624      625 
## 60809.16 64917.79 58526.29 80348.51 65742.58 58211.79 60178.19 55616.25 
##      626      627      628      629      630      631      632      633 
## 46127.88 56640.01 37550.91 37413.69 46807.48 57098.96 55179.23 83384.25 
##      634      635      636      637      638      639      640      641 
## 73749.47 75910.07 77517.50 72340.03 65192.10 61887.80 50013.40 48411.48 
##      642      643      644      645      646      647      648      649 
## 47323.28 45833.22 44247.35 46875.36 51408.51 66106.21 80267.28 77443.13 
##      650      651      652      653      654      655      656      657 
## 78330.40 84094.73 97294.04 92148.30 62960.94 60459.25 57469.21 58435.87 
##      658      659      660      661 
## 54941.79 45529.50 47870.32 52109.31 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8266
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    8.055078  0.5162284     3.55956
## t2* 1820.762702 23.2706832   221.04557
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    3.484781        8.15329   14.96357
## 2    lag_depvar 1503.679097     1830.14658 2224.96432

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Jan 15 01:45:05 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jan 15 01:45:12 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jan 15 01:45:18 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jan 15 01:45:25 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jan 15 01:45:32 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jan 15 01:45:38 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jan 15 01:45:45 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jan 15 01:45:52 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jan 15 01:45:58 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jan 15 01:46:05 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 5.195333 5.410333 5.629750 6.5981458
Comida 366.009167 310.278417 314.087500 346.7794583
Comunicaciones 0.000000 0.000000 0.000000 0.0000000
Electricidad 38.104750 47.072333 38.297667 33.8261667
Enceres 18.259750 20.086417 17.443792 23.0398333
Farmacia 4.733250 1.831667 7.913875 8.6494375
Gas/Bencina 35.219333 44.325000 28.954333 27.5965833
Diosi 55.804250 31.180667 41.934250 44.1985208
donaciones/regalos 0.000000 0.000000 7.170083 5.7233125
Electrodomésticos/ Mantención casa 0.000000 3.944000 30.269500 17.2805833
VTR 12.829167 25.156667 22.121792 19.0466250
Netflix 4.555500 7.151583 7.090167 6.7457708
Otros 0.000000 3.151083 1.575542 0.7877708
Total 540.710500 499.588167 522.488250 540.2722083
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2191, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-12-31 00:04:58 sería de: 37.564 pesos// Percentil 95% más alto proyectado: 40.778,21

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36833.92 36828.51
Lo.80 36883.04 36886.39
Point.Forecast 37564.03 39253.82
Hi.80 39365.36 43947.75
Hi.95 40353.63 46432.57


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(0,0,0) with non-zero mean 
## 
## Coefficients:
##            mean
##       1020.0204
## s.e.    22.4275
## 
## sigma^2 = 30188:  log likelihood = -387.51
## AIC=779.02   AICc=779.24   BIC=783.18
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept    xreg
##       0.2362   835.0084  6.1375
## s.e.  0.1436   294.8750  9.4651
## 
## sigma^2 = 27668:  log likelihood = -377.92
## AIC=763.85   AICc=764.6   BIC=772.09
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 747.5655 679.4817 743.9676
Lo.80 863.5164 797.3541 832.2183
Point.Forecast 1082.5528 1020.0204 1028.4857
Hi.80 1301.5893 1242.6866 1313.6949
Hi.95 1417.6546 1360.5590 1495.4148


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.14         scales_1.3.0        ggiraph_0.8.8      
##  [7] tidytext_0.4.1      DT_0.31             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.4       xts_0.13.1         
## [13] forecast_8.21.1     wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.1     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.3       lubridate_1.9.3     forcats_1.0.0      
## [22] dplyr_1.1.4         purrr_1.0.1         tidyr_1.3.0        
## [25] tibble_3.2.1        ggplot2_3.4.4       tidyverse_2.0.0    
## [28] sjPlot_2.8.15       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-4       sparklyr_1.8.4      httr_1.4.7         
## [34] readxl_1.4.3        zoo_1.8-12          stringr_1.5.1      
## [37] stringi_1.8.3       data.table_1.14.10  reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       lazyeval_0.2.2      splines_4.1.2      
##   [7] crosstalk_1.2.0     digest_0.6.31       htmltools_0.5.5    
##  [10] fansi_1.0.4         ggfortify_0.4.16    magrittr_2.0.3     
##  [13] tzdb_0.4.0          modelr_0.1.11       vroom_1.6.5        
##  [16] askpass_1.1         timechange_0.2.0    anytime_0.3.9      
##  [19] tseries_0.10-55     colorspace_2.1-0    xfun_0.39          
##  [22] crayon_1.5.2        jsonlite_1.8.4      lme4_1.1-35.1      
##  [25] glue_1.6.2          gtable_0.3.4        emmeans_1.9.0      
##  [28] sjstats_0.18.2      sjmisc_2.8.9        car_3.1-2          
##  [31] quantmod_0.4.25     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.1           ggeffects_1.3.4     Rcpp_1.0.10        
##  [37] viridisLite_0.4.2   xtable_1.8-4        performance_0.10.8 
##  [40] bit_4.0.5           htmlwidgets_1.6.2   timeSeries_4032.109
##  [43] gplots_3.1.3        ellipsis_0.3.2      spatial_7.3-14     
##  [46] pkgconfig_2.0.3     farver_2.1.1        nnet_7.3-16        
##  [49] sass_0.4.5          dbplyr_2.4.0        janitor_2.2.0      
##  [52] utf8_1.2.3          tidyselect_1.2.0    labeling_0.4.3     
##  [55] rlang_1.1.3         munsell_0.5.0       cellranger_1.1.0   
##  [58] tools_4.1.2         cachem_1.0.7        cli_3.6.1          
##  [61] generics_0.1.3      sjlabelled_1.2.0    broom_1.0.5        
##  [64] evaluate_0.20       fastmap_1.1.1       yaml_2.3.7         
##  [67] knitr_1.45          bit64_4.0.5         caTools_1.18.2     
##  [70] nlme_3.1-153        slam_0.1-50         xml2_1.3.3         
##  [73] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.14    
##  [76] curl_5.2.0          bslib_0.4.2         highr_0.10         
##  [79] fBasics_4032.96     Matrix_1.6-5        its.analysis_1.6.0 
##  [82] nloptr_2.0.3        urca_1.3-3          vctrs_0.6.5        
##  [85] pillar_1.9.0        lifecycle_1.0.3     lmtest_0.9-40      
##  [88] jquerylib_0.1.4     estimability_1.4.1  bitops_1.0-7       
##  [91] insight_0.19.7      R6_2.5.1            KernSmooth_2.23-20 
##  [94] janeaustenr_1.0.0   codetools_0.2-18    assertthat_0.2.1   
##  [97] boot_1.3-28         MASS_7.3-54         gtools_3.9.5       
## [100] openssl_2.0.6       withr_2.5.2         fracdiff_1.5-2     
## [103] bayestestR_0.13.1   parallel_4.1.2      hms_1.1.3          
## [106] quadprog_1.5-8      timeDate_4032.109   minqa_1.2.6        
## [109] snakecase_0.11.1    rmarkdown_2.25      carData_3.0-5      
## [112] TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))